{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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      "('iteration', 98200, ':l2_error', array([ 1.54838276], dtype=float32))\n",
      "('iteration', 98300, ':l2_error', array([ 1.52424145], dtype=float32))\n",
      "('iteration', 98400, ':l2_error', array([ 1.51747012], dtype=float32))\n",
      "('iteration', 98500, ':l2_error', array([ 1.63517439], dtype=float32))\n",
      "('iteration', 98600, ':l2_error', array([ 1.71550918], dtype=float32))\n",
      "('iteration', 98700, ':l2_error', array([ 1.68466234], dtype=float32))\n",
      "('iteration', 98800, ':l2_error', array([ 1.54669964], dtype=float32))\n",
      "('iteration', 98900, ':l2_error', array([ 1.52314961], dtype=float32))\n",
      "('iteration', 99000, ':l2_error', array([ 1.52178073], dtype=float32))\n",
      "('iteration', 99100, ':l2_error', array([ 1.63668716], dtype=float32))\n",
      "('iteration', 99200, ':l2_error', array([ 1.71274114], dtype=float32))\n",
      "('iteration', 99300, ':l2_error', array([ 1.68091977], dtype=float32))\n",
      "('iteration', 99400, ':l2_error', array([ 1.54597747], dtype=float32))\n",
      "('iteration', 99500, ':l2_error', array([ 1.52020538], dtype=float32))\n",
      "('iteration', 99600, ':l2_error', array([ 1.52210271], dtype=float32))\n",
      "('iteration', 99700, ':l2_error', array([ 1.63322008], dtype=float32))\n",
      "('iteration', 99800, ':l2_error', array([ 1.71293402], dtype=float32))\n",
      "('iteration', 99900, ':l2_error', array([ 1.67575383], dtype=float32))\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'/home/csunix/schtmt/NewFolder/caffe_Sep/python')\n",
    "sys.path.insert(0,'/home/csunix/schtmt/NewFolder/caffe_Sep/examples/mnist_wta_autoencoder')\n",
    "import numpy as np\n",
    "# import matplotlib.pyplot as plt\n",
    "import caffe\n",
    "caffe.set_device(0)\n",
    "caffe.set_mode_gpu()\n",
    "solver = caffe.SGDSolver('mnist_wta_leakyReLU_solver.prototxt')\n",
    "# # #solver.net.params['lstm1'][2].data[15:30]=5\n",
    "# [(k, v.data.shape) for k, v in solver.net.blobs.items()]\n",
    "# # just print the weight sizes (we'll omit the biases)\n",
    "# [(k, v[0].data.shape) for k, v in solver.net.params.items()]\n",
    "#\n",
    "# #solver.net.forward()\n",
    "#\n",
    "niter = 100000\n",
    "train_loss = np.zeros(niter)\n",
    "\n",
    "for i in range(niter):\n",
    "    solver.step(1)\n",
    "#     print solver.net.blobs.keys()\n",
    "#     #train_loss[i] = solver.net.blobs['loss'].data    \n",
    "    loss = solver.net.blobs['l2_error'].data\n",
    "#     print('iteration', i, ':l2_error', loss) \n",
    "    if (i%100 == 0):\n",
    "        print('iteration', i, ':l2_error', loss) \n",
    "    train_loss[i] = loss\n",
    "    np.save('train_loss_leakyReLU_Sep2017',train_loss)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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